A false positive occurs when an experiment appears to show a real improvement even though the observed difference came from random variation, analysis choices, or a flawed test setup.
Repeated peeking changes behavior
Stopping the first time a result looks favorable gives temporary swings more chances to become the final answer. Use the planned stopping rule and record any unplanned analysis separately.
Many comparisons create chances
Every extra metric, segment, and variant offers another opportunity for a lucky result. Keep one primary outcome and treat exploratory segment findings as hypotheses for future tests.
Implementation errors mimic lift
Uneven traffic, lost assignments, bot activity, campaign mismatch, and missing orders can create differences that look statistical. Run sample-ratio, event, and attribution checks before trusting the report.
Replication raises confidence
A large or surprising win deserves a follow-up when the decision is expensive or hard to reverse. Repeating the core idea under new traffic is stronger evidence than adding more post-hoc slices to the first result.
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